wihs

<p>Competing risk data set involving AIDS in women.</p>

datasets

Fast OpenMP parallel computing of Breiman's random forests for univariate, multivariate, unsupervised, survival, competing risks, class imbalanced classification and quantile regression. Extreme random forests and randomized splitting.  Suite of imputation methods for missing data.  Fast random forests using subsampling. Confidence regions and standard errors for variable importance. New improved holdout variable importance. Visualize trees on your Safari or Google Chrome browser.

Udaya Kogalur

randomForestSRC

Fast Unified Random Forests for Survival, Regression, and
Classification (RF-SRC)

wihs function

<p>A data frame containing:</p><table><tr><td>
    time </td>
<td> time to event</td>
</tr><tr><td>
    status </td>
<td> censoring status: 0=censoring, 1=HAART initiation, 2=AIDS/Death before HAART</td>
</tr><tr><td>
    ageatfda </td>
<td> age in years at time of FDA approval of first protease inhibitor</td>
</tr><tr><td>
    idu </td>
<td> history of IDU: 0=no history, 1=history</td>
</tr><tr><td>
    black </td>
<td> race:  0=not African-American; 1=African-American</td>
</tr></table>

wihs: Women's Interagency HIV Study (WIHS)

Description

Arguments

Format

References

Examples

time	time to event
status	censoring status: 0=censoring, 1=HAART initiation, 2=AIDS/Death before HAART
ageatfda	age in years at time of FDA approval of first protease inhibitor
idu	history of IDU: 0=no history, 1=history
black	race: 0=not African-American; 1=African-American